A variety of sophisticated systems have been developed for monitoring and forecasting performance in various fields. These monitoring and forecasting systems may be referred to, collectively or individually, as “estimation systems.” For example, conventional statistics systems, artificial neural network systems, or concurrent learning and information processing (CLIP) systems capable of monitoring and forecasting a large number of variables have been developed for use in a variety of fields including computer performance monitoring and forecasting, visual image processing, electricity demand forecasting, and commodity price forecasting. These estimation systems typically use a number of measured input values to impute (i.e., estimate for a current time trial) current values for monitoring, and they may also predict (i.e., estimate for future time trials) future input values. In particular, these systems may compare imputed input values with actual input values to identify abnormal input values when they occur, and they may also predict or forecast the likelihood that future input values will become abnormal.
The mathematical core technology of these monitoring and forecasting systems may involve the computation of a matrix of estimation parameters, also called learned parameters. This matrix typically contains observed relationships, such as the covariances, between input and output variables. Estimation systems may also utilize the inverse of the covariance matrix, which is sometimes referred to as a “connection weight matrix.” In particular, the elements of the covariance matrix are typically estimated through the application of statistical analysis to a historical database of input and output values. Once the covariance matrix has been computed, it may be inverted to obtain the connection weight matrix, which can be used to directly compute estimates for the output values from a given set of input values through standard matrix computations.
Moreover, once the covariance and connection weight matrices have been initially determined, they may be used to perform monitoring and forecasting on a CLIP basis. That is, a new set of computed output values may be computed for each time trial of measured input values, and for each time trial either the covariance matrix, the connection weight matrix, or both may be updated to achieve learning while the system performs its monitoring and prediction functions. See in particular U.S. Pat. No. 5,835,902.
In connection with using these types of estimation systems for time-based parameters, such as computer performance, expected events are typically superimposed on top of the “normal” system performance without these factors. For example, seasonal holiday events cause an expected deviation from the normal system performance. In addition, scheduled events such as payroll processing quarterly report processing, and system backup operations can also cause an expected deviation from the normal system performance.
Unfortunately, these expected events can cause false alarms in by the computer performance monitoring system. Given the type of events under consideration, staff technicians may often be off work, for example during holidays or scheduled processing performed outside of regular business hours. Such false alarms can therefore be most inconvenient. One approach to addressing this problem is to suspend the computer performance monitoring during these expected events. However, this practice runs the risk of missing a real system problem, which might be exacerbated by the absence of staff technicians.
Moreover, expected events can overlap in time, which can increase the likelihood of false alarms during these periods. Therefore, a continuing need exists for effective and efficient methods and systems for handling expected events, such as seasonal and scheduled events, in estimation systems. A particular need exists for these methods and systems for computer performance estimation systems.